Bi-histogram equalization using modified histogram bins
Graphical abstract
Introduction
Image enhancement remains one of the major concerns in the field of digital image processing. It manipulates an original input image to yield an image with better quality and improved interpretability. As an important image processing technique, image enhancement is extensively used in different applications, such as face recognition [1], watermarking [2], [3], medical image processing [4], and many others [5], [6], [7], [8], [9], [10]. Among the various image enhancement techniques that have been proposed, the technique based on conventional histogram equalization (CHE) is one of the most popular because of its simplicity and effectiveness. The main idea of CHE is to remap the gray levels of the image on the basis of probability density function (PDF). It works by flattening and stretching the dynamic range of the histogram, resulting in an overall enhancement of image contrast [11]. Despite its popularity, CHE suffers from a well-known drawback: it alters the original brightness of the input image. CHE always emphasizes image regions with higher number of gray level occurrences. These regions are frequently over-enhanced. By contrast, regions comprising a relatively small number of pixels may be eliminated, resulting in the so-called washed-out appearance. Several details in the image disappear as the gray levels of the output image decreases. Contrast stretching by CHE is also confined in certain dominated regions. The excessive merging of gray levels of the image results in false contours, which generate undesired artifacts and unnatural enhancement in the image [12]. Saturation problem also occurs where certain local areas are too bright in the output image, thus degrading the outlook of the image and resulting in information loss [13].
Substantial research has been undertaken to address the aforementioned drawbacks. In this work, the problems of mean brightness shifting and domination of high-frequency bins suffered by CHE are addressed. The rest of this paper is organized as follows. Related works on image enhancement, specifically techniques based on histogram equalization (HE), are discussed in Section 2. The research motivation followed by the proposed technique, namely, Bi-histogram Equalization using Modified Histogram Bins (BHEMHB), is outlined in Section 3. The data samples and performance measurement used are discussed in Section 4. The simulation results and discussions are outline in Section 5 using both qualitative and quantitative analyses. Finally, the conclusion of the work is presented in Section 6.
Section snippets
Related works
Several image enhancement techniques based on HE are reviewed for a more comprehensive study. The earliest work to overcome the problem of mean brightness shifting was proposed by Kim [14]. The proposed Brightness Preserving Bi-Histogram Equalization (BBHE) divides the histogram of the input image into two sub-histograms according to mean brightness of the image. Experimental results show that BBHE can reduce the saturation effect and avoid unnatural enhancement and annoying artifacts while
Research motivation and methodology
In this section, the research motivation is presented and the contribution is highlighted. Details of the proposed BHEMHB are also discussed.
Data samples and performance evaluations
This section discusses the data samples used (i.e., standard images from database and microscopic images) in this study. The qualitative and quantitative analyses carried out to evaluate the performance of BHEMHB are also presented.
Results and discussions
In this section, simulation results of the proposed BHEMHB and the seven HE-based techniques are presented. The performance of BHEMHB in enhancing microscopic medical images is then evaluated using cervical cell images.
Conclusion
A novel bi-histogram equalization technique, namely, Bi-histogram Equalization using Modified Histogram Bins (BHEMHB), is proposed in this paper to improve the ability of histogram equalization (HE) in terms of detail and mean brightness preservation. The novelty of BHEMHB is its idea of integrating histogram segmentation with the modification of histogram bins. The proposed technique successfully overcomes the shortcomings of HE, especially in mean brightness and detail preservation. This
Acknowledgements
The authors express their sincere thanks to the anonymous reviewers for their significant contributions to the improvement of the final paper. This study was partially supported by National Cancer Council Malaysia (MAKNA), Malaysia, under the project entitled “Development of an Intelligent Screening System for Cervical Cancer,” and by the Ministry of Higher Education (MOHE), Malaysia under MyPhD Scholarship.
Tang Jing Rui received her B. Eng. degree in Mechatronic Engineering with First Class Honors from Universiti Sains Malaysia (USM), Malaysia in 2012. She is currently a Ph.D. candidate at the School of Electrical and Electronic Engineering, USM and is connected with the Imaging and Intelligent System Research Team (ISRT). Her research interests include digital image processing and intelligent diagnostic systems.
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Tang Jing Rui received her B. Eng. degree in Mechatronic Engineering with First Class Honors from Universiti Sains Malaysia (USM), Malaysia in 2012. She is currently a Ph.D. candidate at the School of Electrical and Electronic Engineering, USM and is connected with the Imaging and Intelligent System Research Team (ISRT). Her research interests include digital image processing and intelligent diagnostic systems.
Nor Ashidi Mat Isa received his B. Eng. degree in Electrical and Electronic Engineering with First Class Honors and Ph.D. degree in Electronic Engineering (majoring in Image Processing and Artificial Neural Network) from USM in 1999 and 2003 respectively. Currently, he serves as a Professor and lectures at the School of Electrical and Electronic Engineering, USM. His research interests include image processing, neural network, intelligent systems, biomedical engineering, and algorithms.